forked from shelhamer/fcn.berkeleyvision.org
-
Notifications
You must be signed in to change notification settings - Fork 0
/
net.py
127 lines (105 loc) · 5.24 KB
/
net.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
import caffe
from caffe import layers as L, params as P
from caffe.coord_map import crop
def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
return conv, L.ReLU(conv, in_place=True)
def max_pool(bottom, ks=2, stride=2):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def fcn(split):
n = caffe.NetSpec()
n.data, n.sem, n.geo = L.Python(module='siftflow_layers',
layer='SIFTFlowSegDataLayer', ntop=3,
param_str=str(dict(siftflow_dir='../data/sift-flow',
split=split, seed=1337)))
# the base net
n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
n.pool1 = max_pool(n.relu1_2)
n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
n.pool2 = max_pool(n.relu2_2)
n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
n.pool3 = max_pool(n.relu3_3)
n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
n.pool4 = max_pool(n.relu4_3)
n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512)
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
n.pool5 = max_pool(n.relu5_3)
# fully conv
n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
n.score_fr_sem = L.Convolution(n.drop7, num_output=33, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.upscore2_sem = L.Deconvolution(n.score_fr_sem,
convolution_param=dict(num_output=33, kernel_size=4, stride=2,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_pool4_sem = L.Convolution(n.pool4, num_output=33, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.score_pool4_semc = crop(n.score_pool4_sem, n.upscore2_sem)
n.fuse_pool4_sem = L.Eltwise(n.upscore2_sem, n.score_pool4_semc,
operation=P.Eltwise.SUM)
n.upscore_pool4_sem = L.Deconvolution(n.fuse_pool4_sem,
convolution_param=dict(num_output=33, kernel_size=4, stride=2,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_pool3_sem = L.Convolution(n.pool3, num_output=33, kernel_size=1,
pad=0, param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2,
decay_mult=0)])
n.score_pool3_semc = crop(n.score_pool3_sem, n.upscore_pool4_sem)
n.fuse_pool3_sem = L.Eltwise(n.upscore_pool4_sem, n.score_pool3_semc,
operation=P.Eltwise.SUM)
n.upscore8_sem = L.Deconvolution(n.fuse_pool3_sem,
convolution_param=dict(num_output=33, kernel_size=16, stride=8,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_sem = crop(n.upscore8_sem, n.data)
# loss to make score happy (o.w. loss_sem)
n.loss = L.SoftmaxWithLoss(n.score_sem, n.sem,
loss_param=dict(normalize=False, ignore_label=255))
n.score_fr_geo = L.Convolution(n.drop7, num_output=3, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.upscore2_geo = L.Deconvolution(n.score_fr_geo,
convolution_param=dict(num_output=3, kernel_size=4, stride=2,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_pool4_geo = L.Convolution(n.pool4, num_output=3, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.score_pool4_geoc = crop(n.score_pool4_geo, n.upscore2_geo)
n.fuse_pool4_geo = L.Eltwise(n.upscore2_geo, n.score_pool4_geoc,
operation=P.Eltwise.SUM)
n.upscore_pool4_geo = L.Deconvolution(n.fuse_pool4_geo,
convolution_param=dict(num_output=3, kernel_size=4, stride=2,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_pool3_geo = L.Convolution(n.pool3, num_output=3, kernel_size=1,
pad=0, param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2,
decay_mult=0)])
n.score_pool3_geoc = crop(n.score_pool3_geo, n.upscore_pool4_geo)
n.fuse_pool3_geo = L.Eltwise(n.upscore_pool4_geo, n.score_pool3_geoc,
operation=P.Eltwise.SUM)
n.upscore8_geo = L.Deconvolution(n.fuse_pool3_geo,
convolution_param=dict(num_output=3, kernel_size=16, stride=8,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_geo = crop(n.upscore8_geo, n.data)
n.loss_geo = L.SoftmaxWithLoss(n.score_geo, n.geo,
loss_param=dict(normalize=False, ignore_label=255))
return n.to_proto()
def make_net():
with open('trainval.prototxt', 'w') as f:
f.write(str(fcn('trainval')))
with open('test.prototxt', 'w') as f:
f.write(str(fcn('test')))
if __name__ == '__main__':
make_net()